
# import libraries
import pandas as pd
import seaborn as sns
import missingno as msno
import statsmodels.formula.api as smf
import statsmodels.api as sm
import matplotlib.pyplot as plt 
import matplotlib
import warnings
warnings.filterwarnings("ignore")

import numpy as np

import shap
from sklearn.model_selection import train_test_split

import plotly.express as px


# init shap
shap.initjs()


# change matplotlib style
plt.style.use('fivethirtyeight')

# set global font-size
matplotlib.rcParams.update({'font.size': 14})

# set font-scale for seaborn chart
sns.set(font_scale=1.5)

# change seaborn plot style
sns.set_style("whitegrid")


# read csv file
used_car_df = pd.read_csv('/Users/zhaoyudong/Downloads/vehicles.csv')


used_car_df.shape

(426880, 26)


used_car_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 426880 entries, 0 to 426879
Data columns (total 26 columns):
 #   Column        Non-Null Count   Dtype  
---  ------        --------------   -----  
 0   id            426880 non-null  int64  
 1   url           426880 non-null  object 
 2   region        426880 non-null  object 
 3   region_url    426880 non-null  object 
 4   price         426880 non-null  int64  
 5   year          425675 non-null  float64
 6   manufacturer  409234 non-null  object 
 7   model         421603 non-null  object 
 8   condition     252776 non-null  object 
 9   cylinders     249202 non-null  object 
 10  fuel          423867 non-null  object 
 11  odometer      422480 non-null  float64
 12  title_status  418638 non-null  object 
 13  transmission  424324 non-null  object 
 14  VIN           265838 non-null  object 
 15  drive         296313 non-null  object 
 16  size          120519 non-null  object 
 17  type          334022 non-null  object 
 18  paint_color   296677 non-null  object 
 19  image_url     426812 non-null  object 
 20  description   426810 non-null  object 
 21  county        0 non-null       float64
 22  state         426880 non-null  object 
 23  lat           420331 non-null  float64
 24  long          420331 non-null  float64
 25  posting_date  426812 non-null  object 
dtypes: float64(5), int64(2), object(19)
memory usage: 84.7+ MB


# check null value
used_car_df.isnull().sum(axis = 0)

id                   0
url                  0
region               0
region_url           0
price                0
year              1205
manufacturer     17646
model             5277
condition       174104
cylinders       177678
fuel              3013
odometer          4400
title_status      8242
transmission      2556
VIN             161042
drive           130567
size            306361
type             92858
paint_color     130203
image_url           68
description         70
county          426880
state                0
lat               6549
long              6549
posting_date        68
dtype: int64


# head 5 row
used_car_df.head()


# backup dataset
used_car_df1 = used_car_df.copy()


# drop unralted columns
used_car_df = used_car_df.loc[:, ['price','year','manufacturer','condition','cylinders','fuel',
                                           'odometer','transmission','drive','size','type','paint_color','state']]


# show matrix plot of different fields, check null values percentage
msno.matrix(used_car_df, figsize=(13, 5), color=(0,0,0.5))

<AxesSubplot:>


# drop null values
used_car_df = used_car_df.dropna(subset=['price','year','condition','manufacturer',
                           'cylinders','fuel','odometer','transmission',
                           'drive','paint_color','type','size'], how='any')


used_car_df.shape

(80170, 13)


used_car_df.head()


plt.boxplot(used_car_df.odometer)
plt.xlabel('odometer')
plt.show()


plt.boxplot(used_car_df.price)
plt.xlabel('price')
plt.show()


plt.hist(used_car_df['price'],bins=60)
plt.xlabel('price')
plt.title('Distribution of price')
plt.show()


plt.hist(used_car_df['odometer'],bins=60)
plt.xlabel('odometer')
plt.title('Distribution of odometer')
plt.show()


cols = ['price', 'odometer']


used_car_df[cols].describe()


# trim outlier
upper_limit = used_car_df[cols].quantile(0.99)
lower_limit = used_car_df[cols].quantile(0.01)


used_car_df_trimed = used_car_df[~(((used_car_df[cols] <= lower_limit)
                            |(used_car_df[cols] >= upper_limit)).any(axis=1))]


used_car_df_trimed.shape

(74476, 13)


### define a group of tools

def draw_multiple_price_boxplots(df, group_field, title='', xlabel='', filter_labels=[], orient="v"):

    if len(filter_labels) != 0: 
        df = df[df[group_field].isin(filter_labels)]
    size = (8, 8)
    if len(df[group_field].unique()) >= 8:
        size = (12, 8) if orient != 'h' else (8, 12)
    fig,ax = plt.subplots(figsize=size)
    x,y = group_field, "price"
    if orient == 'h':
        x,y = y,x
    sns.boxplot(x=x, y=y, data=df, showfliers=False, ax=ax, orient=orient)
    ax.set(
        axisbelow=True,  # Hide the grid behind plot objects
        title=title
    )
    

def _draw_bar_plot(df, field, title='', xlabel='', ylabel=''):
    fig, ax = plt.subplots(figsize=(12,10))
    ax.bar(df[field], df['price'], width=1, edgecolor="white", linewidth=0.7)
    ax.set(
        title=title,
        xlabel=xlabel,
        ylabel=ylabel
    )
    plt.show()
    
def draw_bar_plot(df, group_field, title='', xlabel='', ylabel=''):
    grouped = df.groupby([group_field]).count()
    grouped = grouped.reset_index()
    _draw_bar_plot(grouped, group_field, title, xlabel, ylabel)

def draw_head_bar_plot(df, group_field, head=10, title='', xlabel='', ylabel=''):
    grouped = df.groupby([group_field]).count()
    grouped = grouped.reset_index()
    grouped = grouped.head(head)
    _draw_bar_plot(grouped, group_field, title, xlabel, ylabel)
    
def draw_pie_plot(df, group_field, title=''):
    grouped = df[group_field].value_counts()/len(df) * 100
    fig, ax = plt.subplots(figsize=(10,10))
    ax.pie(grouped)
    labels = [f'{l}, {s:0.1f}%' for l, s in zip(grouped.index, grouped)]
    plt.legend(bbox_to_anchor=(1,0.5), loc="center right", labels=labels,
               bbox_transform=plt.gcf().transFigure)
    plt.savefig("output.png", bbox_inches="tight")
    plt.subplots_adjust(left=0.0, bottom=0.2, right=0.9)
    plt.title(title)
    plt.show()

def draw_line_plot(x, y):
    fig, ax = plt.subplots(figsize=(10,10))
    ax.plot(x, y, linewidth=2.0)
    plt.show()


# price hist
fig,ax = plt.subplots(figsize=(16,10))
sns.histplot(data=used_car_df_trimed, x="price", kde=True, ax=ax)
ax.set(title='Frequency Distribution of Price')

[Text(0.5, 1.0, 'Frequency Distribution of Price')]


# year and confition
fig,ax = plt.subplots(figsize=(16,10))
sns.histplot(data=used_car_df_trimed,
             x="year", hue="condition",kde=True,  multiple="stack", ax=ax)
ax.set(title='Frequency Distribution of Year')

[Text(0.5, 1.0, 'Frequency Distribution of Year')]


grouped_by_year_df = used_car_df_trimed.groupby(['year'])
grouped_by_year_df.price.describe()


used_car_df_trimed.odometer.describe()

count     74476.000000
mean     117839.591949
std       58083.058896
min         203.000000
25%       76278.000000
50%      115831.000000
75%      156000.000000
max      295747.000000
Name: odometer, dtype: float64


plt.boxplot(used_car_df_trimed.odometer)
plt.xlabel('odometer')
plt.show()


# odometer hist
fig,ax = plt.subplots(figsize=(16,10))
sns.histplot(data=used_car_df_trimed, x="odometer", kde=True, ax=ax)
ax.set(title='Frequency Distribution of Odometer')

[Text(0.5, 1.0, 'Frequency Distribution of Odometer')]


sns.jointplot(x=used_car_df_trimed['odometer'], y=used_car_df_trimed['price'], height=10, kind="hex", color="#4CB391")

<seaborn.axisgrid.JointGrid at 0x7f89cb6a2d00>


used_car_df_trimed.condition.value_counts()

excellent    36758
good         24453
like new      9777
fair          2907
new            356
salvage        225
Name: condition, dtype: int64


used_car_df_trimed.condition.unique()

array(['excellent', 'good', 'new', 'fair', 'like new', 'salvage'],
      dtype=object)


grouped_by_condition_df = used_car_df_trimed.groupby(['condition'])
grouped_by_condition_df.price.describe()


draw_pie_plot(used_car_df_trimed, 'condition', title='Piechart of Condition')


draw_multiple_price_boxplots(used_car_df_trimed, 'condition', 
                             'Boxplots of price of different fuel', 'fuel')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="condition", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8
)

<seaborn.axisgrid.FacetGrid at 0x7f8aca8dadf0>


used_car_df_trimed.manufacturer.unique()

array(['ford', 'honda', 'dodge', 'chrysler', 'toyota', 'jeep', 'lexus',
       'chevrolet', 'bmw', 'gmc', 'mercedes-benz', 'mazda', 'rover',
       'ram', 'nissan', 'audi', 'mitsubishi', 'infiniti', 'volkswagen',
       'kia', 'hyundai', 'fiat', 'acura', 'cadillac', 'lincoln', 'jaguar',
       'saturn', 'volvo', 'alfa-romeo', 'buick', 'subaru', 'mini',
       'pontiac', 'harley-davidson', 'porsche', 'tesla', 'mercury',
       'datsun', 'land rover', 'aston-martin', 'ferrari'], dtype=object)


used_car_df_trimed.manufacturer.value_counts()

ford               14383
chevrolet          11170
toyota              6549
honda               4657
nissan              3809
jeep                3064
gmc                 2899
ram                 2658
dodge               2357
bmw                 2101
subaru              1901
mercedes-benz       1813
volkswagen          1717
hyundai             1667
kia                 1315
chrysler            1309
lexus               1198
cadillac            1151
buick               1000
mazda                940
audi                 867
pontiac              700
acura                669
lincoln              630
infiniti             615
mitsubishi           596
volvo                579
mini                 411
mercury              390
rover                355
saturn               341
porsche              221
jaguar               208
fiat                 125
harley-davidson       31
alfa-romeo            28
tesla                 25
datsun                14
land rover             8
aston-martin           3
ferrari                2
Name: manufacturer, dtype: int64


grouped_by_manu_df = used_car_df_trimed.groupby(['manufacturer'])
grouped_by_manu_df.price.describe()


draw_head_bar_plot(used_car_df_trimed, 'manufacturer', title = 'Barchart of Manufacturer')


top_10_manufacturer = used_car_df_trimed.manufacturer.value_counts().head(10).index.tolist()
draw_multiple_price_boxplots(used_car_df_trimed, 'manufacturer', 
                             'Boxplots of price of different manufacturer',
                             'manufacturer',filter_labels=top_10_manufacturer)


used_car_df_trimed.fuel.unique()

array(['gas', 'diesel', 'hybrid', 'electric', 'other'], dtype=object)


used_car_df_trimed.fuel.value_counts()

gas         68714
diesel       4633
hybrid        920
electric      117
other          92
Name: fuel, dtype: int64


grouped_by_fuel_df = used_car_df_trimed.groupby(['fuel'])
grouped_by_fuel_df.price.describe()


used_car_df_trimed.fuel.value_counts().index

Index(['gas', 'diesel', 'hybrid', 'electric', 'other'], dtype='object')


draw_pie_plot(used_car_df_trimed,'fuel', "Piechart of Fuel Types")


draw_multiple_price_boxplots(used_car_df_trimed, 'fuel', 
                             'Boxplots of price of different fuel', 'fuel')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="fuel", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8
)

<seaborn.axisgrid.FacetGrid at 0x7f89b40cd700>


used_car_df_trimed.drive.unique()

array(['rwd', '4wd', 'fwd'], dtype=object)


used_car_df_trimed.drive.value_counts()

4wd    30985
fwd    27000
rwd    16491
Name: drive, dtype: int64


grouped_by_drive_df = used_car_df_trimed.groupby(['drive'])
grouped_by_drive_df.price.describe()


draw_pie_plot(used_car_df_trimed, 'drive', "Piechart of Drive")


draw_bar_plot(used_car_df_trimed, 'drive', "Piechart of Drive")


draw_multiple_price_boxplots(used_car_df_trimed, 'drive', 
                             'Boxplots of price of different drive', 'drive')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="drive", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8
)

<seaborn.axisgrid.FacetGrid at 0x7f8acabd3d30>


used_car_df_trimed.paint_color.unique()

array(['black', 'blue', 'silver', 'white', 'grey', 'yellow', 'red',
       'green', 'brown', 'purple', 'custom', 'orange'], dtype=object)


used_car_df_trimed.paint_color.value_counts()

white     17963
black     13015
silver    10901
grey       8993
blue       7978
red        7507
green      2546
brown      2370
custom     1932
yellow      602
orange      423
purple      246
Name: paint_color, dtype: int64


grouped_by_color_df = used_car_df_trimed.groupby(['paint_color'])
grouped_by_color_df.price.describe()


draw_pie_plot(used_car_df_trimed,'paint_color', "Piechart of Paint Color")


draw_bar_plot(used_car_df_trimed,'paint_color', "Bar Chart of Paint Color")


draw_multiple_price_boxplots(used_car_df_trimed, 'paint_color', 
                             'Boxplots of price of different paint color', 'paint_color')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="paint_color", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8, aspect=1.5
)

<seaborn.axisgrid.FacetGrid at 0x7f89b6342490>


used_car_df_trimed.type.unique()

array(['truck', 'pickup', 'mini-van', 'sedan', 'offroad', 'SUV',
       'convertible', 'coupe', 'hatchback', 'wagon', 'other', 'van',
       'bus'], dtype=object)


used_car_df_trimed.type.value_counts()

sedan          20880
SUV            19061
truck          12182
pickup          6161
coupe           4082
hatchback       3041
van             2621
convertible     2134
mini-van        1794
wagon           1610
other            462
offroad          326
bus              122
Name: type, dtype: int64


grouped_by_type_df = used_car_df_trimed.groupby(['type'])
grouped_by_type_df.price.describe()


draw_pie_plot(used_car_df_trimed,'type', "Pie Chart of Types")


draw_bar_plot(used_car_df_trimed,'type', "Bar Chart of Types")


draw_multiple_price_boxplots(used_car_df_trimed, 'type', 
                             'Boxplots of price of different types', 'type', orient="h")


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="type", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8, aspect=1.5
)

<seaborn.axisgrid.FacetGrid at 0x7f89b8c84070>


used_car_df_trimed.transmission.unique()

array(['automatic', 'manual', 'other'], dtype=object)


used_car_df_trimed.transmission.value_counts()

automatic    68046
manual        5561
other          869
Name: transmission, dtype: int64


grouped_by_transmission_df = used_car_df_trimed.groupby(['transmission'])
grouped_by_transmission_df.price.describe()


draw_pie_plot(used_car_df_trimed,'transmission', "PieChart of Transmission Types")


draw_multiple_price_boxplots(used_car_df_trimed, 'transmission', 
                             'Boxplots of price of different transmission', 'transmission')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="transmission", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8
)

<seaborn.axisgrid.FacetGrid at 0x7f89b8c84670>


used_car_df_trimed['size'].unique()

array(['full-size', 'mid-size', 'compact', 'sub-compact'], dtype=object)


used_car_df_trimed['size'].value_counts()

full-size      41399
mid-size       22212
compact         9575
sub-compact     1290
Name: size, dtype: int64


grouped_by_size_df = used_car_df_trimed.groupby(['size'])
grouped_by_size_df.price.describe()


draw_pie_plot(used_car_df_trimed,'size', 'Piechart of Size')


draw_multiple_price_boxplots(used_car_df_trimed, 'size', 'Boxplots of price of different size')


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="size", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8
)

<seaborn.axisgrid.FacetGrid at 0x7f89b73c8f70>


used_car_df_trimed.cylinders.unique()

array(['6 cylinders', '8 cylinders', '4 cylinders', '5 cylinders',
       '10 cylinders', '3 cylinders', 'other', '12 cylinders'],
      dtype=object)


used_car_df_trimed.cylinders.value_counts()

6 cylinders     25474
4 cylinders     24914
8 cylinders     22231
10 cylinders      757
5 cylinders       697
other             224
3 cylinders       138
12 cylinders       41
Name: cylinders, dtype: int64


grouped_by_cylinders_df = used_car_df_trimed.groupby(['cylinders'])
grouped_by_cylinders_df.price.describe()


draw_pie_plot(used_car_df_trimed, 'cylinders', 'Piechart of Cylinders')


draw_multiple_price_boxplots(used_car_df_trimed, 'cylinders', 'Boxplots of price of different clinders', orient="h")


sns.catplot(
    data=used_car_df_trimed, kind="bar",
    x="cylinders", y="price", ax=ax,
    ci="sd", palette="dark", alpha=.6, height=8, aspect=1.3
)

<seaborn.axisgrid.FacetGrid at 0x7f89b9a94df0>


# create grouped dataframe for plotly
groupby_state_df = used_car_df_trimed.groupby(['state'])

groupby_state_df_count = groupby_state_df.count()
groupby_state_df_count['count'] = groupby_state_df_count['price']
groupby_state_df_count = groupby_state_df_count.reset_index()

groupby_state_df_Mean = groupby_state_df.mean()
groupby_state_df_Mean = groupby_state_df_Mean.reset_index()
groupby_state_df_Mean = groupby_state_df_Mean.loc[:,['state', 'price']].merge(
    groupby_state_df_count.loc[:,['state', 'count']], left_on='state', right_on='state')

groupby_state_df_Mean['state'] = groupby_state_df_Mean['state'].str.upper()


# draw U.S. heat map

fig = px.choropleth(groupby_state_df_Mean, 
                    locations="state",  
                    color="count", 
                    hover_name="state", # DataFrame column hover info
                    locationmode = 'USA-states') # Set to plot as US States
fig.update_layout(
    title_text = 'Frequency Distribution', 
    geo_scope='usa', 
)
fig.show(renderer="notebook")


# draw U.S. heat map
fig = px.choropleth(groupby_state_df_Mean, 
                    locations="state",  
                    color="price", 
                    hover_name="state", # DataFrame column hover info
                    locationmode = 'USA-states') # Set to plot as US States
fig.update_layout(
    title_text = 'Average price of different state', 
    geo_scope='usa', 
)
fig.show(renderer="notebook")


# create dummies value
def categorical_2_ordered_dummies(df, field, orderList):
    df[field] = df[field].astype('category')
    df[field] = df[field].cat.reorder_categories(orderList, ordered=True)
    df[field] = df[field].cat.codes


    
## ordered
#  conditions
categorical_2_ordered_dummies(used_car_df_trimed, 'condition', 
                             ['salvage','fair','good','excellent', 'like new', 'new'])
# size
categorical_2_ordered_dummies(used_car_df_trimed, 'size', 
                             ['sub-compact', 'compact', 'mid-size', 'full-size'])


def categorical_one_hot_encoding(df, fields):
    prefixs = []
    for k in fields:
        prefixs.append(k + '_')
    return pd.get_dummies(df, columns=fields, prefix=prefixs)
    
# non-ordered
used_car_df_trimed = categorical_one_hot_encoding(used_car_df_trimed,
                             ['manufacturer','cylinders','fuel','transmission','drive','type','paint_color','state'])


# adjust year
used_car_df_trimed['year'] = 2022 - used_car_df_trimed['year']


# create train data and test data
y = used_car_df_trimed.iloc[:,0]
X = used_car_df_trimed.iloc[:,1:]
X = sm.add_constant(X)
# a simple linear model
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01, random_state=10)


# train statsmodel 
model = sm.OLS(y_train, X_train).fit()

# print summary
print(model.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                  price   R-squared:                       0.607
Model:                            OLS   Adj. R-squared:                  0.606
Method:                 Least Squares   F-statistic:                     860.9
Date:                Sun, 12 Dec 2021   Prob (F-statistic):               0.00
Time:                        01:37:29   Log-Likelihood:            -7.5512e+05
No. Observations:               73731   AIC:                         1.511e+06
Df Residuals:                   73598   BIC:                         1.512e+06
Df Model:                         132                                         
Covariance Type:            nonrobust                                         
=================================================================================================
                                    coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------------------------
const                          9205.9471    162.084     56.797      0.000    8888.263    9523.632
year                           -239.9513      3.099    -77.426      0.000    -246.026    -233.877
condition                      1502.8824     36.222     41.491      0.000    1431.888    1573.877
odometer                         -0.0830      0.000   -170.769      0.000      -0.084      -0.082
size                            850.1660     41.115     20.678      0.000     769.581     930.751
manufacturer__acura             489.4520    317.303      1.543      0.123    -132.460    1111.364
manufacturer__alfa-romeo       3642.4523   1268.236      2.872      0.004    1156.714    6128.190
manufacturer__aston-martin     2.062e+04   3896.439      5.291      0.000     1.3e+04    2.83e+04
manufacturer__audi             -818.4172    293.700     -2.787      0.005   -1394.069    -242.766
manufacturer__bmw             -1533.7300    236.459     -6.486      0.000   -1997.189   -1070.271
manufacturer__buick           -1245.0643    282.321     -4.410      0.000   -1798.413    -691.716
manufacturer__cadillac        -1829.7672    271.524     -6.739      0.000   -2361.954   -1297.580
manufacturer__chevrolet        -799.4309    196.046     -4.078      0.000   -1183.680    -415.182
manufacturer__chrysler        -3294.1199    263.461    -12.503      0.000   -3810.503   -2777.737
manufacturer__datsun           7469.5536   1785.344      4.184      0.000    3970.287     1.1e+04
manufacturer__dodge           -2851.7473    230.993    -12.346      0.000   -3304.494   -2399.001
manufacturer__ferrari           698.2963   4719.884      0.148      0.882   -8552.659    9949.252
manufacturer__fiat            -5266.7346    632.388     -8.328      0.000   -6506.213   -4027.256
manufacturer__ford            -1028.0803    194.215     -5.294      0.000   -1408.741    -647.419
manufacturer__gmc               309.9876    226.097      1.371      0.170    -133.162     753.137
manufacturer__harley-davidson -5573.3800   1208.848     -4.610      0.000   -7942.717   -3204.043
manufacturer__honda             414.1870    210.772      1.965      0.049       1.075     827.299
manufacturer__hyundai         -1977.9036    247.703     -7.985      0.000   -2463.401   -1492.406
manufacturer__infiniti        -1127.7709    327.814     -3.440      0.001   -1770.285    -485.257
manufacturer__jaguar          -2454.3720    498.575     -4.923      0.000   -3431.578   -1477.166
manufacturer__jeep             -558.4166    224.821     -2.484      0.013    -999.064    -117.769
manufacturer__kia             -2261.7342    262.116     -8.629      0.000   -2775.481   -1747.987
manufacturer__land rover       3516.8861   2352.058      1.495      0.135   -1093.138    8126.910
manufacturer__lexus            1631.4187    267.911      6.089      0.000    1106.313    2156.524
manufacturer__lincoln         -3305.2072    324.833    -10.175      0.000   -3941.879   -2668.536
manufacturer__mazda           -1120.6321    286.446     -3.912      0.000   -1682.064    -559.200
manufacturer__mercedes-benz   -1144.2408    242.191     -4.725      0.000   -1618.934    -669.548
manufacturer__mercury         -3427.1097    386.127     -8.876      0.000   -4183.918   -2670.302
manufacturer__mini            -2254.0713    383.237     -5.882      0.000   -3005.213   -1502.929
manufacturer__mitsubishi      -2090.2062    334.122     -6.256      0.000   -2745.085   -1435.328
manufacturer__nissan          -2744.4790    213.337    -12.865      0.000   -3162.619   -2326.339
manufacturer__pontiac           -71.4204    314.549     -0.227      0.820    -687.934     545.094
manufacturer__porsche          5516.0865    488.195     11.299      0.000    4559.227    6472.946
manufacturer__ram                24.0140    233.348      0.103      0.918    -433.347     481.375
manufacturer__rover            1250.5503    401.178      3.117      0.002     464.243    2036.857
manufacturer__saturn          -3189.6203    406.796     -7.841      0.000   -3986.939   -2392.302
manufacturer__subaru          -1274.7857    250.798     -5.083      0.000   -1766.349    -783.223
manufacturer__tesla            1.666e+04   1474.001     11.305      0.000    1.38e+04    1.96e+04
manufacturer__toyota           1735.4495    203.308      8.536      0.000    1336.967    2133.932
manufacturer__volkswagen      -1497.5133    248.747     -6.020      0.000   -1985.057   -1009.969
manufacturer__volvo             -33.0345    357.933     -0.092      0.926    -734.582     668.513
cylinders__10 cylinders        1740.9182    281.560      6.183      0.000    1189.061    2292.775
cylinders__12 cylinders        4662.3925    980.073      4.757      0.000    2741.452    6583.333
cylinders__3 cylinders        -2221.7926    538.565     -4.125      0.000   -3277.378   -1166.207
cylinders__4 cylinders         -373.3274    180.077     -2.073      0.038    -726.278     -20.377
cylinders__5 cylinders         -589.0464    309.329     -1.904      0.057   -1195.330      17.237
cylinders__6 cylinders         1284.6111    177.525      7.236      0.000     936.662    1632.560
cylinders__8 cylinders         3645.5774    181.694     20.064      0.000    3289.458    4001.697
cylinders__other               1056.6144    496.574      2.128      0.033      83.331    2029.898
fuel__diesel                   9646.7616    224.765     42.919      0.000    9206.223    1.01e+04
fuel__electric                 1509.8867    650.469      2.321      0.020     234.971    2784.803
fuel__gas                      -541.2850    206.372     -2.623      0.009    -945.774    -136.796
fuel__hybrid                   -255.4935    276.168     -0.925      0.355    -796.781     285.794
fuel__other                   -1153.9226    608.008     -1.898      0.058   -2345.616      37.770
transmission__automatic        5918.1730     96.853     61.105      0.000    5728.341    6108.005
transmission__manual           6939.5100    114.629     60.539      0.000    6714.838    7164.182
transmission__other           -3651.7359    180.369    -20.246      0.000   -4005.259   -3298.213
drive__4wd                     4900.6225     71.531     68.511      0.000    4760.422    5040.823
drive__fwd                     1185.8007     76.229     15.556      0.000    1036.393    1335.209
drive__rwd                     3119.5239     74.429     41.913      0.000    2973.643    3265.405
type__SUV                      -384.9482     89.354     -4.308      0.000    -560.082    -209.814
type__bus                     -1225.7479    581.733     -2.107      0.035   -2365.942     -85.554
type__convertible              2447.7848    162.320     15.080      0.000    2129.637    2765.932
type__coupe                    2113.0129    125.592     16.824      0.000    1866.853    2359.172
type__hatchback               -1392.7030    144.310     -9.651      0.000   -1675.551   -1109.855
type__mini-van                  285.1080    176.874      1.612      0.107     -61.564     631.780
type__offroad                  2493.9785    365.974      6.815      0.000    1776.670    3211.286
type__other                    -589.1339    302.269     -1.949      0.051   -1181.581       3.313
type__pickup                   2436.9951    117.238     20.787      0.000    2207.208    2666.782
type__sedan                   -1798.9650     88.201    -20.396      0.000   -1971.838   -1626.092
type__truck                    4659.3627    103.987     44.807      0.000    4455.549    4863.177
type__van                      2216.4083    148.045     14.971      0.000    1926.241    2506.575
type__wagon                   -2055.2052    180.916    -11.360      0.000   -2409.800   -1700.610
paint_color__black             1266.0532     79.960     15.834      0.000    1109.332    1422.774
paint_color__blue               238.6170     90.366      2.641      0.008      61.500     415.734
paint_color__brown             -433.0147    141.484     -3.061      0.002    -710.322    -155.707
paint_color__custom             880.7736    153.773      5.728      0.000     579.379    1182.168
paint_color__green              -31.2454    137.516     -0.227      0.820    -300.776     238.286
paint_color__grey               539.5172     88.179      6.118      0.000     366.686     712.348
paint_color__orange            2860.5261    310.894      9.201      0.000    2251.175    3469.877
paint_color__purple             837.0642    402.256      2.081      0.037      48.643    1625.485
paint_color__red                210.5496     92.286      2.281      0.023      29.669     391.430
paint_color__silver             124.9586     83.470      1.497      0.134     -38.642     288.559
paint_color__white             1138.1976     76.061     14.964      0.000     989.118    1287.277
paint_color__yellow            1573.9502    262.474      5.997      0.000    1059.503    2088.398
state__ak                      3537.8431    274.946     12.867      0.000    2998.950    4076.737
state__al                      2263.1827    240.859      9.396      0.000    1791.100    2735.265
state__ar                       701.4458    298.903      2.347      0.019     115.596    1287.295
state__az                       123.0685    173.241      0.710      0.477    -216.484     462.621
state__ca                      1000.8654     85.700     11.679      0.000     832.893    1168.837
state__co                        50.0138    165.348      0.302      0.762    -274.068     374.096
state__ct                     -1362.9261    229.156     -5.948      0.000   -1812.070    -913.782
state__dc                     -1547.6719    307.149     -5.039      0.000   -2149.683    -945.661
state__de                       527.3768    447.063      1.180      0.238    -348.866    1403.619
state__fl                     -1652.9823     98.020    -16.864      0.000   -1845.101   -1460.864
state__ga                       940.5338    233.873      4.022      0.000     482.143    1398.924
state__hi                       825.4556    326.839      2.526      0.012     184.853    1466.058
state__ia                      -155.2052    170.199     -0.912      0.362    -488.794     178.384
state__id                       242.6718    185.845      1.306      0.192    -121.583     606.927
state__il                      -457.8805    172.399     -2.656      0.008    -795.782    -119.979
state__in                      -679.3883    194.390     -3.495      0.000   -1060.392    -298.385
state__ks                       451.1065    190.140      2.372      0.018      78.433     823.780
state__ky                       656.8721    213.429      3.078      0.002     238.553    1075.192
state__la                     -1108.0410    359.408     -3.083      0.002   -1812.478    -403.604
state__ma                     -1221.9875    170.137     -7.182      0.000   -1555.456    -888.519
state__md                      -632.5970    276.777     -2.286      0.022   -1175.078     -90.116
state__me                       431.7520    283.408      1.523      0.128    -123.727     987.231
state__mi                      1119.5608    133.734      8.372      0.000     857.443    1381.679
state__mn                       137.0114    157.570      0.870      0.385    -171.825     445.848
state__mo                       809.8195    257.045      3.150      0.002     306.012    1313.627
state__ms                       529.7345    473.196      1.119      0.263    -397.728    1457.197
state__mt                      1355.5604    229.414      5.909      0.000     905.909    1805.212
state__nc                      1357.2805    136.219      9.964      0.000    1090.292    1624.269
state__nd                      1925.2895    462.123      4.166      0.000    1019.531    2831.048
state__ne                      -207.1048    412.154     -0.502      0.615   -1014.924     600.715
state__nh                     -1293.0699    289.117     -4.472      0.000   -1859.739    -726.401
state__nj                     -1380.9116    158.536     -8.710      0.000   -1691.641   -1070.182
state__nm                        45.7290    247.931      0.184      0.854    -440.215     531.673
state__nv                      -931.9171    305.720     -3.048      0.002   -1531.127    -332.707
state__ny                     -1415.0552    110.944    -12.755      0.000   -1632.506   -1197.604
state__oh                     -1672.3734    118.323    -14.134      0.000   -1904.287   -1440.460
state__ok                       748.2047    201.246      3.718      0.000     353.764    1142.646
state__or                       609.8799    151.490      4.026      0.000     312.959     906.801
state__pa                     -1673.6724    134.841    -12.412      0.000   -1937.959   -1409.385
state__ri                     -1402.8743    276.550     -5.073      0.000   -1944.912    -860.837
state__sc                       994.9875    204.553      4.864      0.000     594.064    1395.912
state__sd                       665.2882    386.432      1.722      0.085     -92.118    1422.694
state__tn                      2362.1600    166.348     14.200      0.000    2036.118    2688.202
state__tx                      -396.0159    119.783     -3.306      0.001    -630.789    -161.242
state__ut                      2646.7747    464.082      5.703      0.000    1737.176    3556.373
state__va                       -89.6070    154.361     -0.581      0.562    -392.154     212.940
state__vt                       516.2568    221.729      2.328      0.020      81.668     950.845
state__wa                       114.4259    216.419      0.529      0.597    -309.755     538.607
state__wi                        41.7150    141.816      0.294      0.769    -236.244     319.674
state__wv                      -599.9337    575.004     -1.043      0.297   -1726.939     527.072
state__wy                      1355.2962    574.887      2.357      0.018     228.520    2482.073
==============================================================================
Omnibus:                    10684.901   Durbin-Watson:                   1.999
Prob(Omnibus):                  0.000   Jarque-Bera (JB):            43522.323
Skew:                           0.677   Prob(JB):                         0.00
Kurtosis:                       6.512   Cond. No.                     3.52e+17
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.03e-20. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.


y_pred = model.predict(X_test)


from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred)

45024232.60457944


# compute the SHAP values for the linear model
explainer = shap.Explainer(model.predict, X_train)
# explainer process will take several minutes, please wait!
shap_values = explainer(X_test)

Permutation explainer: 746it [00:18, 14.19it/s]


shap.plots.bar(shap_values, max_display = 15)


shap.plots.heatmap(shap_values, max_display = 15)


shap.plots.beeswarm(shap_values, max_display = 15)


# explain sample 1
shap.plots.force(shap_values[0])


shap.plots.waterfall(shap_values[0])


used_car_df[used_car_df.index==y_test.index[0]]


# explain sample 2
shap.plots.force(shap_values[110])


used_car_df[used_car_df.index==y_test.index[110]]


shap.plots.waterfall(shap_values[110])

	id	url	region	region_url	price	year	manufacturer	model	condition	cylinders	...	size	type	paint_color	image_url	description	county	state	lat	long	posting_date
0	7222695916	https://prescott.craigslist.org/cto/d/prescott...	prescott	https://prescott.craigslist.org	6000	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	az	NaN	NaN	NaN
1	7218891961	https://fayar.craigslist.org/ctd/d/bentonville...	fayetteville	https://fayar.craigslist.org	11900	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	ar	NaN	NaN	NaN
2	7221797935	https://keys.craigslist.org/cto/d/summerland-k...	florida keys	https://keys.craigslist.org	21000	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	fl	NaN	NaN	NaN
3	7222270760	https://worcester.craigslist.org/cto/d/west-br...	worcester / central MA	https://worcester.craigslist.org	1500	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	ma	NaN	NaN	NaN
4	7210384030	https://greensboro.craigslist.org/cto/d/trinit...	greensboro	https://greensboro.craigslist.org	4900	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	nc	NaN	NaN	NaN

	price	odometer
count	8.017000e+04	8.017000e+04
mean	7.953919e+04	1.248147e+05
std	1.379153e+07	2.494849e+05
min	0.000000e+00	0.000000e+00
25%	4.950000e+03	7.300000e+04
50%	9.000000e+03	1.140000e+05
75%	1.750000e+04	1.554675e+05
max	3.736929e+09	1.000000e+07

	count	mean	std	min	25%	50%	75%	max
year
1900.0	1.0	38250.000000	NaN	38250.0	38250.0	38250.0	38250.0	38250.0
1905.0	1.0	3990.000000	NaN	3990.0	3990.0	3990.0	3990.0	3990.0
1916.0	1.0	12000.000000	NaN	12000.0	12000.0	12000.0	12000.0	12000.0
1918.0	1.0	16000.000000	NaN	16000.0	16000.0	16000.0	16000.0	16000.0
1923.0	5.0	20298.000000	4522.888458	15000.0	17500.0	18990.0	25000.0	25000.0
...	...	...	...	...	...	...	...	...
2017.0	3399.0	22906.838188	11897.517403	1.0	14896.5	20980.0	31500.0	55900.0
2018.0	2620.0	26285.083969	13062.719164	1.0	17450.0	25256.0	36490.0	56000.0
2019.0	1622.0	28541.700986	13234.619328	1.0	19995.0	29900.0	36700.0	56499.0
2020.0	745.0	30739.477852	12365.196406	1.0	21900.0	30000.0	38500.0	56000.0
2021.0	83.0	33243.554217	14177.420484	50.0	24995.0	32900.0	44750.0	56000.0

	count	mean	std	min	25%	50%	75%	max
condition
excellent	36758.0	14202.414903	10603.938666	1.0	6600.0	10995.0	18900.0	56000.0
fair	2907.0	3798.018232	3651.584091	1.0	1550.0	2800.0	4700.0	55000.0
good	24453.0	10782.673578	9567.596169	1.0	4400.0	7490.0	13900.0	56000.0
like new	9777.0	17956.718728	12579.254665	1.0	7995.0	14900.0	25750.0	56499.0
new	356.0	18389.994382	15387.959027	1.0	5995.0	13997.0	29050.0	55000.0
salvage	225.0	4045.231111	4590.818723	1.0	1000.0	2000.0	5900.0	25000.0

	count	mean	std	min	25%	50%	75%	max
manufacturer
acura	669.0	9385.112108	7204.492183	1.0	4700.00	7395.0	11999.00	46567.0
alfa-romeo	28.0	21795.964286	12485.364651	2000.0	9375.00	26800.0	28887.50	54998.0
aston-martin	3.0	42631.666667	9555.182276	32900.0	37947.50	42995.0	47497.50	52000.0
audi	867.0	13087.080738	8960.137121	1.0	6500.00	10999.0	16995.00	48000.0
bmw	2101.0	12371.571157	9771.248331	1.0	5900.00	9998.0	16350.00	55000.0
buick	1000.0	9640.080000	8413.417134	1.0	3950.00	6999.0	12995.00	50000.0
cadillac	1151.0	11637.825369	9539.912961	1.0	4999.00	9991.0	15000.00	53995.0
chevrolet	11170.0	14983.314772	11483.029943	1.0	5999.00	11750.0	21700.00	56000.0
chrysler	1309.0	7657.862490	5736.425782	1.0	3695.00	6250.0	9990.00	51500.0
datsun	14.0	13434.928571	7516.606082	2500.0	8248.00	15000.0	18800.00	26500.0
dodge	2357.0	10207.248197	9148.096780	1.0	4200.00	7500.0	12995.00	55900.0
ferrari	2.0	21500.500000	30404.884484	1.0	10750.75	21500.5	32250.25	43000.0
fiat	125.0	9099.544000	5357.066749	1.0	5500.00	7500.0	10950.00	32500.0
ford	14383.0	16129.381701	12363.794811	1.0	6500.00	12500.0	23495.00	56499.0
gmc	2899.0	18741.705761	13102.827331	1.0	8495.00	15000.0	27500.00	55680.0
harley-davidson	31.0	13801.612903	5332.392213	3000.0	11975.00	11975.0	17475.00	25950.0
honda	4657.0	8878.240713	6413.336155	1.0	4500.00	7000.0	11900.00	55000.0
hyundai	1667.0	8250.660468	5090.428855	1.0	4500.00	7400.0	10900.00	35995.0
infiniti	615.0	12112.582114	8990.761344	1.0	5800.00	9500.0	16997.00	52000.0
jaguar	208.0	11555.153846	9578.759443	1.0	5298.75	7900.0	14988.50	42995.0
jeep	3064.0	13772.554830	10399.795310	1.0	5500.00	11984.0	19900.00	54995.0
kia	1315.0	8650.286692	5685.056397	30.0	4900.00	7898.0	11000.00	55000.0
land rover	8.0	18116.875000	20783.590013	3950.0	6371.25	8247.5	19498.75	55000.0
lexus	1198.0	13183.651920	9573.382715	1.0	6861.25	10500.0	16998.00	51000.0
lincoln	630.0	9713.228571	7182.786736	1.0	4800.00	7797.5	13966.00	50985.0
mazda	940.0	8399.646809	5790.062169	1.0	4500.00	6925.0	9999.25	34500.0
mercedes-benz	1813.0	13116.517375	10631.268623	1.0	5850.00	9995.0	17995.00	55000.0
mercury	390.0	5601.289744	4978.931002	1.0	2756.25	3999.0	6495.00	35000.0
mini	411.0	9292.732360	5223.749597	1.0	5950.00	7995.0	10900.00	34595.0
mitsubishi	596.0	13272.395973	11462.055735	1.0	4150.00	7999.5	24990.00	39900.0
nissan	3809.0	9676.215280	6932.500816	1.0	4990.00	7995.0	12900.00	51000.0
pontiac	700.0	8142.174286	8275.697440	1.0	3500.00	4995.0	8525.00	46500.0
porsche	221.0	21236.963801	12373.857633	1.0	12000.00	18000.0	28000.00	56000.0
ram	2658.0	22670.181716	13884.901137	1.0	11000.00	20990.0	32995.00	56000.0
rover	355.0	18255.811268	13373.402910	1.0	8000.00	15500.0	23900.00	55812.0
saturn	341.0	4536.299120	2863.110247	300.0	2995.00	3995.0	5200.00	22000.0
subaru	1901.0	10301.986323	6855.840268	1.0	5499.00	8500.0	13990.00	43995.0
tesla	25.0	34804.400000	15983.478248	1.0	28500.00	36400.0	45000.00	54750.0
toyota	6549.0	12214.268133	9962.557170	1.0	5460.00	8900.0	15900.00	55000.0
volkswagen	1717.0	9278.158998	6120.498103	1.0	5000.00	7950.0	11995.00	40000.0
volvo	579.0	8188.341969	7972.708722	1.0	3550.00	5900.0	9850.00	52991.0

Import¶

Dataset Shape & Data Cleaning¶

Discriptive Stat¶

Price¶

Year¶

Odometer¶

Condition¶

manufacturer¶

Fuel¶

Drive¶

paint_color¶

type¶

transmission¶

Size¶

Cylinders¶

State　¶

Linear regression¶

	price	year	manufacturer	condition	cylinders	fuel	odometer	transmission	drive	size	type	paint_color	state
31	15000	2013.0	ford	excellent	6 cylinders	gas	128000.0	automatic	rwd	full-size	truck	black	al
55	19900	2004.0	ford	good	8 cylinders	diesel	88000.0	automatic	4wd	full-size	pickup	blue	al
59	14000	2012.0	honda	excellent	6 cylinders	gas	95000.0	automatic	fwd	full-size	mini-van	silver	al
65	22500	2001.0	ford	good	8 cylinders	diesel	144700.0	manual	rwd	full-size	truck	white	al
73	15000	2017.0	dodge	excellent	8 cylinders	gas	90000.0	automatic	rwd	mid-size	sedan	grey	al

	count	mean	std	min	25%	50%	75%	max
fuel
diesel	4633.0	27146.822577	14315.667955	1.0	15000.0	26990.0	38900.00	56499.0
electric	117.0	18387.470085	14846.362473	1100.0	7200.0	12999.0	23500.00	54750.0
gas	68714.0	12243.165323	9891.205969	1.0	5000.0	8997.0	16500.00	56000.0
hybrid	920.0	10334.448913	7614.810203	1.0	5700.0	7998.5	12499.00	49900.0
other	92.0	11681.076087	8501.509141	5.0	4937.5	9900.0	15923.75	45000.0

	count	mean	std	min	25%	50%	75%	max
drive
4wd	30985.0	16785.611522	12418.355071	1.0	7000.0	13900.0	23995.0	56499.0
fwd	27000.0	8184.754296	5911.396897	1.0	4200.0	6888.0	10495.0	56000.0
rwd	16491.0	14474.030562	10752.132758	1.0	6000.0	11500.0	20825.0	55995.0

	count	mean	std	min	25%	50%	75%	max
paint_color
black	13015.0	14352.902497	11278.745947	1.0	6200.0	10995.0	19000.00	56000.0
blue	7978.0	11125.945976	9245.276472	1.0	4900.0	7995.0	14900.00	55000.0
brown	2370.0	10074.659494	8849.902685	1.0	4062.5	7490.0	12990.00	52995.0
custom	1932.0	12265.525880	10355.845933	1.0	4999.0	8894.0	15995.00	55000.0
green	2546.0	9446.562058	9063.892368	1.0	3500.0	6200.0	11950.00	55950.0
grey	8993.0	12450.698543	9978.807525	1.0	5500.0	9300.0	16000.00	56000.0
orange	423.0	14776.439716	10962.077589	1.0	5999.5	11995.0	21000.00	55000.0
purple	246.0	9423.300813	9492.141901	200.0	3815.0	6000.0	10999.75	55000.0
red	7507.0	12494.775410	10633.082820	1.0	4995.0	8995.0	16500.00	55500.0
silver	10901.0	10948.618751	9228.395040	1.0	4900.0	7950.0	13999.00	56000.0
white	17963.0	16202.734398	12136.043584	1.0	6850.0	12988.0	23900.00	56499.0
yellow	602.0	13047.308970	9970.903523	1.0	5500.0	9997.5	18000.00	54500.0

	count	mean	std	min	25%	50%	75%	max
type
SUV	19061.0	12611.693353	9354.211886	1.0	5975.00	9998.0	16888.00	56000.0
bus	122.0	12264.950820	8989.174139	175.0	4700.00	11500.0	18495.00	32500.0
convertible	2134.0	13840.753046	9813.212157	1.0	6800.00	11000.0	18500.00	55000.0
coupe	4082.0	12576.124449	10831.705343	1.0	4800.00	8990.0	17500.00	55995.0
hatchback	3041.0	8256.286748	5636.887320	1.0	4500.00	6900.0	10000.00	50000.0
mini-van	1794.0	8893.641026	7733.397366	1.0	3896.25	6200.0	11500.00	54000.0
offroad	326.0	16297.392638	11216.922923	13.0	7500.00	13500.0	23388.00	54500.0
other	462.0	10856.374459	10011.339976	1.0	3825.00	7997.5	14500.00	52500.0
pickup	6161.0	17535.816751	12220.757982	1.0	7995.00	14994.0	24995.00	56000.0
sedan	20880.0	8347.595307	6445.277854	1.0	4100.00	6950.0	10500.00	55900.0
truck	12182.0	22077.240108	13383.732300	1.0	10950.00	20925.0	31900.00	56499.0
van	2621.0	15982.635254	10370.203447	1.0	7495.00	13995.0	22487.00	55000.0
wagon	1610.0	7738.832919	6616.183921	1.0	3400.00	6498.5	10387.25	54000.0

	count	mean	std	min	25%	50%	75%	max
transmission
automatic	68046.0	13367.647915	10880.182871	1.0	5500.0	9900.0	17995.0	56499.0
manual	5561.0	11426.911707	9733.070516	1.0	4500.0	8000.0	15900.0	55500.0
other	869.0	7619.966628	10950.993805	1.0	1.0	100.0	12500.0	50000.0

	count	mean	std	min	25%	50%	75%	max
size
compact	9575.0	8684.537859	7133.975688	1.0	4000.00	6988.0	11000.0	55995.0
full-size	41399.0	15620.299838	12020.057764	1.0	6495.00	11999.0	22000.0	56499.0
mid-size	22212.0	10713.082433	8488.268715	1.0	4950.00	7999.0	13995.0	56000.0
sub-compact	1290.0	9305.075194	7306.001295	1.0	4599.25	6999.0	11999.0	55000.0

	count	mean	std	min	25%	50%	75%	max
cylinders
10 cylinders	757.0	15994.978864	15111.774061	1.0	1.00	11997.0	26990.00	55000.0
12 cylinders	41.0	15947.634146	12236.549912	1.0	9300.00	11500.0	19900.00	52000.0
3 cylinders	138.0	9828.985507	7740.449053	1.0	4923.75	8995.0	10998.50	34000.0
4 cylinders	24914.0	9586.935016	6833.039030	1.0	4975.00	7976.0	12873.25	55995.0
5 cylinders	697.0	7123.466284	5150.060638	1.0	3988.00	6000.0	8500.00	42000.0
6 cylinders	25474.0	12638.406964	10697.934649	1.0	4988.00	8988.0	17495.00	56000.0
8 cylinders	22231.0	17838.730107	12669.053547	1.0	7705.50	14975.0	25990.00	56499.0
other	224.0	14847.241071	12483.084141	1000.0	5950.00	10000.0	20918.50	54750.0

Import¶

Dataset Shape & Data Cleaning¶

Discriptive Stat¶

Price¶

Year¶

Odometer¶

Condition¶

manufacturer¶

Fuel¶

Drive¶

paint_color¶

type¶

transmission¶

Size¶

Cylinders¶

State ¶

Linear regression¶

State　¶